Neural network classifiers for automated video surveillance

Jan, T; Piccardi, M; Hintz, T

Neural network classifiers for automated video surveillance

Jan, T Piccardi, M

Hintz, T

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Publication Type:: Conference Proceeding
Citation:: Neural Networks for Signal Processing - Proceedings of the IEEE Workshop, 2003, 2003-January pp. 729 - 738
Issue Date:: 2003-01-01

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Field	Value	Language
dc.contributor.author	Jan, T	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.contributor.author	Hintz, T	en_US
dc.date.issued	2003-01-01	en_US
dc.identifier.citation	Neural Networks for Signal Processing - Proceedings of the IEEE Workshop, 2003, 2003-January pp. 729 - 738	en_US
dc.identifier.isbn	0780381777	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3167
dc.description.abstract	© 2003 IEEE. In automated visual surveillance applications, detection of suspicious human behaviors is of great practical importance. However due to random nature of human movements, reliable classification of suspicious human movements can be very difficult. Artificial Neural Network (ANN) classifiers can perform well however their computational requirements can be very large for real time implementation. In this paper, a data-based modeling neural network such as Modified Probabilistic Neural Network (MPNN) is introduced which partitions the decision space nonlinearly in order to achieve reliable classification, however still with acceptable computations. The experiment shows that the compact MPNN attains good classification performance compared to t h a t of other larger conventional neural network based classifiers such as Multilayer Perceptron (MLP) and Self Organising Map (SOM).	en_US
dc.relation.ispartof	Neural Networks for Signal Processing - Proceedings of the IEEE Workshop	en_US
dc.relation.isbasedon	10.1109/NNSP.2003.1318072	en_US
dc.title	Neural network classifiers for automated video surveillance	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2003-January	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Toulouse, France	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2003-January	en_US

Abstract:

© 2003 IEEE. In automated visual surveillance applications, detection of suspicious human behaviors is of great practical importance. However due to random nature of human movements, reliable classification of suspicious human movements can be very difficult. Artificial Neural Network (ANN) classifiers can perform well however their computational requirements can be very large for real time implementation. In this paper, a data-based modeling neural network such as Modified Probabilistic Neural Network (MPNN) is introduced which partitions the decision space nonlinearly in order to achieve reliable classification, however still with acceptable computations. The experiment shows that the compact MPNN attains good classification performance compared to t h a t of other larger conventional neural network based classifiers such as Multilayer Perceptron (MLP) and Self Organising Map (SOM).

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http://hdl.handle.net/10453/3167